The Benefits of Reusing Batches for Gradient Descent in Two-Layer
Networks: Breaking the Curse of Information and Leap Exponents

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

5 February 2024

Emanuele Troiani

Lenka Zdeborová

Florent Krzakala

Papers citing "The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents"

7 / 7 papers shown

Title
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input Ziang Chen Rong Ge MLT 59 1 0 10 Jan 2025
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence Berfin Simsek Amire Bendjeddou Daniel Hsu 44 0 0 13 Nov 2024
Robust Feature Learning for Multi-Index Models in High Dimensions Alireza Mousavi-Hosseini Adel Javanmard Murat A. Erdogdu OOD AAML 42 1 0 21 Oct 2024
Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics Alireza Mousavi-Hosseini Denny Wu Murat A. Erdogdu MLT AI4CE 27 6 0 14 Aug 2024
Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions Luca Arnaboldi Yatin Dandi Florent Krzakala Luca Pesce Ludovic Stephan 68 12 0 24 May 2024
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar C. Pehlevan 139 201 0 07 Feb 2020
Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach Grant M. Rotskoff Eric Vanden-Eijnden 59 118 0 02 May 2018